Supplementary Material for: Liquid Biopsy: Value for Melanoma Therapy?

<div>Treatment of metastatic melanoma has undergone tremendous changes over the past few years. There are now highly effective systemic therapies available: targeted therapy with BRAF and MEK inhibitors in BRAF-V600-mutant melanoma and immunotherapies, </div><div>including PD-1 antibodies with or without CTLA-4 antibody, that can be used regardless of mutational status. However, long-term tumor control is only achieved in a minority of patients. Liquid biopsy using circulating tumor cells, circulating tumor DNA (ctDNA), circulating mRNA and micro-RNA might represent valuable biomarkers, e.g. in the setting of systemic treatment for metastatic melanoma. Pre-treatment detection of BRAF V600-mutant ctDNA has been shown to be a prognostic factor in patients receiving BRAF/MEK inhibitor treatment. Furthermore, monitoring of ctDNA of known driver mutations can be used for treatment monitoring and detection of acquired resistance. However, results of the currently available studies need to be interpreted with caution as multiple approaches were used that are hardly comparable. So far, even with advancement of methods, there are only prognostic but no predictive biomarkers available.</div

Man against Machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Background: Deep learning convolutional neural networks (CNN) May facilitate melanoma detection, but data comparing a CNN\u2019s diagnostic performance to larger groups of dermatologists are lacking. Methods: Google\u2019s Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists\u2019 diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN\u2019s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (6standard deviation) sensitivity and specificity for lesion classification of 86.6% (69.3%) and 71.3% (611.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (69.6%, P \ubc 0.19) and specificity to 75.7% (611.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN\u2019s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians\u2019 experience, they May benefit from assistance by a CNN\u2019s image classification

Chemotherapy after immune checkpoint inhibitor failure in metastatic melanoma: a retrospective multicentre analysis.

Despite remarkably improved outcomes with immune checkpoint inhibition, many patients with metastatic melanoma will eventually require further therapy. Chemotherapy has limited activity when used first-line but can alter the tumour microenvironment and does improve efficacy when used in combination with immunotherapy in lung cancer. Whether chemotherapy after checkpoint inhibitor failure has relevant activity in patients with metastatic melanoma is unknown. Patients with metastatic melanoma treated with chemotherapy after progression on immunotherapy with checkpoint inhibitors were identified retrospectively from 24 melanoma centres. Objective response rate (ORR), progression-free survival (PFS), overall survival (OS) and safety were examined. In total, 463 patients were treated between 2007 and 2017. Fifty-six per cent had received PD-1-based therapy before chemotherapy. Chemotherapy regimens included carboplatin + paclitaxel (32%), dacarbazine (25%), temozolomide (15%), taxanes (9%, nab-paclitaxel 4%), fotemustine (6%) and others (13%). Median duration of therapy was 7.9 weeks (0-108). Responses included 0.4% complete response (CR), 12% partial response (PR), 21% stable disease (SD) and 67% progressive disease (PD). Median PFS was 2.6 months (2.2, 3.0), and median PFS in responders was 8.7 months (6.3, 16.3), respectively. Twelve-month PFS was 12% (95% CI 2-15%). In patients who had received anti-PD-1 before chemotherapy, the ORR was 11%, and median PFS was 2.5 months (2.1, 2.8). The highest activity was achieved with single-agent taxanes (N = 40), with ORR 25% and median PFS 3.9 months (2.1, 6.2). Median OS from chemotherapy start was 7.1 months (6.5, 8.0). Subsequent treatment with checkpoint inhibitors achieved a response rate of 16% with a median PFS of 19.1 months (2.0-43.1 months). No unexpected toxicities were observed. Chemotherapy has a low response rate and short PFS in patients with metastatic melanoma who have failed checkpoint inhibitor therapy, although activity varied between regimens. Chemotherapy has a limited role in the management of metastatic melanoma